I am working on opencv eye detection project and i have sucessfully detect rectangular region of both eyes through the help of haar cascade for boths eyes. now i want to detect the eye balls from both eyes, the problem is that i have no haar cascade for eye ball tracking. kindly help me if anyone of you have this xml and suggest other solution.
here is my code of eye detection
private Mat get_template(CascadeClassifier clasificator, Rect area,int size)
{
Mat eye = new Mat();
Mat template = new Mat();
Mat mROI = mGray.submat(area);
MatOfRect eyes = new MatOfRect();
Point iris = new Point();
Rect eye_template = new Rect();
clasificator.detectMultiScale(mROI, eyes, 1.15, 2, Objdetect.CASCADE_FIND_BIGGEST_OBJECT|Objdetect.CASCADE_SCALE_IMAGE, new Size(30,30), new Size());
Rect[] eyesArray = eyes.toArray();
for (int i = 0; i < eyesArray.length; i++)
{
Rect e = eyesArray[i];
e.x = area.x + e.x;
e.y = area.y + e.y;
Core.rectangle(mROI, e.tl(), e.br(), new Scalar(25, 50, 0, 255));
Rect eye_only_rectangle = new Rect((int)e.tl().x, (int)( e.tl().y + e.height*0.4), (int)e.width, (int)(e.height*0.6));
//reduce ROI
mROI = mGray.submat(eye_only_rectangle);
Mat vyrez = mRgba.submat(eye_only_rectangle);
Core.MinMaxLocResult mmG = Core.minMaxLoc(mROI);
//Draw pink circle on eyeball
int radius = vyrez.height()/2;
// Core.circle(vyrez, mmG.minLoc, 2, new Scalar(0, 255, 0, 1), radius);
//Core.circle(vyrez, mmG.minLoc,2, new Scalar(255, 0, 255),1);
iris.x = mmG.minLoc.x + eye_only_rectangle.x;
iris.y = mmG.minLoc.y + eye_only_rectangle.y;
eye_template = new Rect((int)iris.x-size/2,(int)iris.y-size/2 ,size,size);
//draw red rectangle around eyeball
//Core.rectangle(mRgba,eye_template.tl(),eye_template.br(),new Scalar(255, 0, 0, 255), 2);
eye = (mRgba.submat(eye_only_rectangle));
template = (mGray.submat(eye_template)).clone();
//return template;
Mat eyeball_HSV = new Mat();
Mat dest = new Mat();
//Mat eye = new Mat();
//eye = mRgba.submat(eye_only_rectangle);
List<Mat> hsv_channel = new ArrayList<Mat>();
//convert image to HSV
Imgproc.cvtColor(eye, eyeball_HSV, Imgproc.COLOR_RGB2HSV, 0);
// get HSV channel
//hsv_channel[0] is hue
//hsv_channel[1] is saturation
//hsv_channel[2] is visibility
Core.split(eyeball_HSV, hsv_channel);
try
{
hsv_channel.get(0).setTo(new Scalar(145));
Log.v(TAG, "Got the Channel!");
}
catch(Exception ex)
{
ex.printStackTrace();
Log.v(TAG, "Didn't get any channel");
}
Core.merge(hsv_channel, eyeball_HSV);
Imgproc.cvtColor(eyeball_HSV, dest, Imgproc.COLOR_HSV2RGB);
Imgproc.cvtColor(dest, eye, Imgproc.COLOR_RGB2RGBA);
}
return eye;
}`enter code here`
If you are willing to consider other solutions then haar cascades, you can use facial landmark detection code. Facial landmark packages can give the location of the eyes in the image (usually, center of the eye and left and right borders).
Examples of landmark detection packages:
STASM:
http://www.milbo.users.sonic.net/stasm/
Flandmark detector:
http://cmp.felk.cvut.cz/~uricamic/flandmark/
Related
Im detecting patches squares at strip image
I think boundaries are not clear
How can i get this patches clearly
please help me
this is my code and images
Thanks
Code
Bitmap bmp = BitmapFactory.decodeResource(getResources(), R.drawable.src2);
// bmp = changeBitmapContrastBrightness(bmp, (float)1.5, 0);
Mat src = new Mat();
Utils.bitmapToMat(bmp, src);
// Creating an empty matrix to store the result
Mat dst = new Mat();
// Creating kernel matrix
Mat kernel = Mat.ones(1,1, CvType.CV_32F);
for(int i = 0; i<kernel.rows(); i++) {
for(int j = 0; j<kernel.cols(); j++) {
double[] m = kernel.get(i, j);
for(int k = 1; k<m.length; k++) {
m[k] = m[k]/(2 * 2);
}
kernel.put(i,j, m);
}
}
Imgproc.filter2D(src, dst, -1, kernel);
Imgproc.cvtColor(dst, dst, Imgproc.COLOR_BGR2GRAY);
// Preparing the kernel matrix object
Mat kernel1 = Imgproc.getStructuringElement(Imgproc.MORPH_RECT,
new Size((2*2) + 1, (2*2)+1));
Imgproc.dilate(dst, dst, kernel1);
Imgproc.threshold(dst, dst, 160, 255, Imgproc.THRESH_BINARY);
// Creating kernel matrix
Mat kernel2 = Mat.ones(5,5, CvType.CV_32F);
Imgproc.morphologyEx(dst, dst, Imgproc.MORPH_OPEN, kernel2);
}
private static List<MatOfPoint> contourFind(Mat img){
List<MatOfPoint> contours = new ArrayList<>();
Imgproc.findContours(img, contours, new Mat(), Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
List<MatOfPoint> squares = new ArrayList<>();
for(MatOfPoint cnt: contours){
MatOfPoint2f curve = new MatOfPoint2f(cnt.toArray());
MatOfPoint2f approxCurve = new MatOfPoint2f();
Imgproc.approxPolyDP(curve, approxCurve, 0.02 * Imgproc.arcLength(curve, true), true);
int numberVertices = (int) approxCurve.total();
double contourArea = Imgproc.contourArea(cnt);
if (Math.abs(contourArea) < img.size().area() / 10){
squares.add(cnt);
}
}
return squares;
}
Original Image
enter image description here
After process Image
enter image description here
You might want to look at some segmentation algorithm and train your model based on sample images for each category. There are algorithms like the Watershed algorithm for classical Machine learning. Or look at semantic segmentation if you can use deep learning and neural networks.
I'm working on Android and OpenCV 3.2, I want to apply the perspective transform but I have some trouble
I referenced to this official document OpenCV (go to perspective transform example ) http://docs.opencv.org/trunk/da/d6e/tutorial_py_geometric_transformations.html
but i get a rotated image result 90° or 180°
intput img = [input img ][2] output img output image 90 ° output 2 [output image 180 °][4]
this is my code
public void prespective(Bitmap img){
Mat imgSrc = new Mat();
Bitmap bmp32 = img.copy(Bitmap.Config.ARGB_8888, true);
Utils.bitmapToMat(bmp32, imgSrc);
Mat gray = new Mat();
cvtColor( imgSrc, gray, COLOR_RGBA2GRAY ); //Convert to gray
Mat thr = new Mat();
threshold( gray, thr, 125, 255, THRESH_BINARY ); //Threshold the gray
List<MatOfPoint> contours = new LinkedList<>(); // Vector for storing contours
findContours( thr, contours,new Mat(), RETR_CCOMP, CHAIN_APPROX_SIMPLE ); // Find the contours in the image
MatOfPoint m = biggestCountousMatOfPoint(contours);
RotatedRect box = Imgproc.minAreaRect(new MatOfPoint2f(m.toArray()));
Rect destImageRect = box.boundingRect();
Mat destImage = new Mat(destImageRect.size(),imgSrc.type());
final Point[] pts = new Point[4];
box.points(pts);
Mat _src = new MatOfPoint2f(pts[0], pts[1], pts[2], pts[3]);
Mat _dst = new MatOfPoint2f(new Point(0, 0), new Point(destImage.width() - 1, 0), new Point(destImage.width() - 1, destImage.height() - 1), new Point(0, destImage.height() - 1));
Mat perspectiveTransform=Imgproc.getPerspectiveTransform(_src, _dst);
Imgproc.warpPerspective(imgSrc, destImage, perspectiveTransform, destImage.size());
if(destImage.height() > 0 && destImage.width() > 0) {
Bitmap cinBitmapRotated = Bitmap.createBitmap(destImage.width(), destImage.height(), Bitmap.Config.ARGB_8888);
if (cinBitmapRotated != null) {
Utils.matToBitmap(destImage, cinBitmapRotated);
prenom2Cin.setImageBitmap(cinBitmapRotated);
}
}
}
what's the problem in my code ?
I am trying to draw the contour of every element in a picture with separated musical notations.
This is the code that I am running in android/java:
public static Bitmap findNotationContours(Bitmap inputImage) {
Mat inputImageMat = new Mat();
Utils.bitmapToMat(inputImage, inputImageMat);
Imgproc.cvtColor(inputImageMat, inputImageMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(inputImageMat, inputImageMat, new Size(5, 5), 0);
Imgproc.adaptiveThreshold(inputImageMat, inputImageMat, 255, 1, 1, 11, 2);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(inputImageMat, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
int contourColor = android.R.color.holo_red_dark;
Scalar contourScalar = new Scalar(Color.red(contourColor), Color.green(contourColor), Color.blue(contourColor));
for (int i = 0; i < contours.size(); i++) {
Rect rect = Imgproc.boundingRect(contours.get(i));
Imgproc.rectangle(inputImageMat,
new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
contourScalar, 3);
}
Utils.matToBitmap(inputImageMat, inputImage);
return inputImage;
}
The result I am getting is:
If you zoom in enough you can see that the contour for the notations are there, but I need to keep the original picture with just a rectangle contour around each of them, so I can save those as a pattern.
Can you please tell me what I am doing wrong?
With the crucial information from #Dan Mašek the problem was fixed, I will add the revised code that needs no explanation, just that we keep a Mat with the initial 3-channel image which we use to draw the contour rectangles on.
public static Bitmap findNotationContours(Bitmap inputImage) {
Mat inputImageMat = new Mat();
Mat resultImageMat = new Mat();
Utils.bitmapToMat(inputImage, inputImageMat);
Utils.bitmapToMat(inputImage, resultImageMat);
Imgproc.cvtColor(inputImageMat, inputImageMat, Imgproc.COLOR_BGR2GRAY);
Imgproc.GaussianBlur(inputImageMat, inputImageMat, new Size(5, 5), 0);
Imgproc.adaptiveThreshold(inputImageMat, inputImageMat, 255, 1, 1, 11, 2);
List<MatOfPoint> contours = new ArrayList<>();
Mat hierarchy = new Mat();
Imgproc.findContours(inputImageMat, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_SIMPLE);
Scalar contourScalar = new Scalar(255,0,0);
for (int i = 0; i < contours.size(); i++) {
Rect rect = Imgproc.boundingRect(contours.get(i));
Imgproc.rectangle(resultImageMat,
new Point(rect.x, rect.y),
new Point(rect.x + rect.width, rect.y + rect.height),
contourScalar, 3);
Log.i("contour", "contour" + i + " x:" + rect.x + " y:" + rect.y);
}
Utils.matToBitmap(resultImageMat, inputImage);
return inputImage;
}
final result
Currently I'm developing an app that will detect colored circles. I'm trying to do this by following this tutorial, where guy detects red circles on image with Python. I've written the same code, just for Java.
Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(),
CvType.CV_8UC3);
Mat hsv_image = new Mat();
Utils.bitmapToMat(bitmap, mat);
Imgproc.cvtColor(mat, hsv_image, Imgproc.COLOR_BGR2HSV);
Mat lower_red_hue_range = new Mat();
Mat upper_red_hue_range = new Mat();
Core.inRange(hsv_image, new Scalar(0, 100, 100), new Scalar(10, 255, 255), lower_red_hue_range);
Core.inRange(hsv_image, new Scalar(160, 100, 100), new Scalar(179, 255, 255), upper_red_hue_range);
Utils.matToBitmap(hsv_image, bitmap);
mutableBitmap = bitmap.copy(Bitmap.Config.ARGB_8888, true);
image.setImageBitmap(mutableBitmap);
Image I use is identical to one from tutorial:
This is image with applied BGR2HSV:
When I execute the code using lower red hue range, it detects the blue circle. When I use upper red hue range it gives me black bmp(doesn't detect anything). How can it be? What am I doing wrong? This is literally copy moved from python to Java. Why's the result different then?
Thanks in advance.
Your mat is of CvType.CV_8UC1 image, i.e. you are working on a grayscale image. Try with CvType.CV_8UC3
Mat mat = new Mat(bitmap.getWidth(), bitmap.getHeight(), CvType.CV_8UC3);
hsv_image should look like this:
How to select a custom range:
You may want to detect a green circle.
Well, in HSV, tipically the range is:
H in [0,360]
S,V in [0,100]
However, for CV_8UC3 images, each component H,S,V can be represented by only 256 values at most, since it's stored in 1 byte. So, in OpenCV, the ranges H,S,V for CV_8UC3 are:
H in [0,180] <- halved to fit in the range
S,V in [0,255] <- stretched to fit the range
So to switch from typical range to OpenCV range you need to:
opencv_H = typical_H / 2;
opencv_S = typical_S * 2.55;
opencv_V = typical_V * 2.55;
So, green colors are around the value of hue of 120. The hue can have a value in the interval [0,360].
However, for Mat3b HSV images, the range for H is in [0,180], i.e. is halved so it can fit in a 8 bit representation with at most 256 possible values.
So, you want the H value to be around 120 / 2 = 60, say from 50 to 70.
You also set a minimum value for S,V to 100 in order to prevent very dark (almost black) colors.
Mat green_hue_range
inRange(hsv_image, cv::Scalar(50, 100, 100), cv::Scalar(70, 255, 255), green_hue_range);
use the following code and pass color to Blob detector and then pass an image to the detector
private Scalar converScalarRgba2HSV(Scalar rgba) {
Mat pointMatHsv= new Mat();
Mat pointMatRgba = new Mat(1, 1, CvType.CV_8UC3, rgba);
Imgproc.cvtColor(pointMatRgba,pointMatHsv, Imgproc.COLOR_RGB2HSV_FULL, 4);
return new Scalar(pointMatHsv.get(0, 0));}
// Blob Detector
public class ColorBlobDetector {
// Lower and Upper bounds for range checking in HSV color space
private Scalar mLowerBound = new Scalar(0);
private Scalar mUpperBound = new Scalar(0);
// Minimum contour area in percent for contours filtering
private static double mMinContourArea = 0.1;
// Color radius for range checking in HSV color space
private Scalar mColorRadius = new Scalar(25,50,50,0);
private Mat mSpectrum = new Mat();
private List<MatOfPoint> mContours = new ArrayList<MatOfPoint>();
Mat mPyrDownMat = new Mat();
Mat mHsvMat = new Mat();
Mat mMask = new Mat();
Mat mDilatedMask = new Mat();
Mat mHierarchy = new Mat();
public void setColorRadius(Scalar radius) {
mColorRadius = radius;
}
public void setHsvColor(Scalar hsvColor) {
double minH = (hsvColor.val[0] >= mColorRadius.val[0]) ? hsvColor.val[0]-mColorRadius.val[0] : 0;
double maxH = (hsvColor.val[0]+mColorRadius.val[0] <= 255) ? hsvColor.val[0]+mColorRadius.val[0] : 255;
mLowerBound.val[0] = minH;
mUpperBound.val[0] = maxH;
mLowerBound.val[1] = hsvColor.val[1] - mColorRadius.val[1];
mUpperBound.val[1] = hsvColor.val[1] + mColorRadius.val[1];
mLowerBound.val[2] = hsvColor.val[2] - mColorRadius.val[2];
mUpperBound.val[2] = hsvColor.val[2] + mColorRadius.val[2];
mLowerBound.val[3] = 0;
mUpperBound.val[3] = 255;
Mat spectrumHsv = new Mat(1, (int)(maxH-minH), CvType.CV_8UC3);
for (int j = 0; j < maxH-minH; j++) {
byte[] tmp = {(byte)(minH+j), (byte)255, (byte)255};
spectrumHsv.put(0, j, tmp);
}
Imgproc.cvtColor(spectrumHsv, mSpectrum, Imgproc.COLOR_HSV2RGB_FULL, 4);
}
public Mat getSpectrum() {
return mSpectrum;
}
public void setMinContourArea(double area) {
mMinContourArea = area;
}
public void process(Mat rgbaImage) {
Imgproc.pyrDown(rgbaImage, mPyrDownMat);
Imgproc.pyrDown(mPyrDownMat, mPyrDownMat);
Imgproc.cvtColor(mPyrDownMat, mHsvMat, Imgproc.COLOR_RGB2HSV_FULL);
Core.inRange(mHsvMat, mLowerBound, mUpperBound, mMask);
Imgproc.dilate(mMask, mDilatedMask, new Mat());
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Imgproc.findContours(mDilatedMask, contours, mHierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
// Find max contour area
double maxArea = 0;
Iterator<MatOfPoint> each = contours.iterator();
while (each.hasNext()) {
MatOfPoint wrapper = each.next();
double area = Imgproc.contourArea(wrapper);
if (area > maxArea)
maxArea = area;
}
// Filter contours by area and resize to fit the original image size
mContours.clear();
each = contours.iterator();
while (each.hasNext()) {
MatOfPoint contour = each.next();
if (Imgproc.contourArea(contour) > mMinContourArea*maxArea) {
Core.multiply(contour, new Scalar(4,4), contour);
mContours.add(contour);
}
}
}
public List<MatOfPoint> getContours() {
return mContours;
}}
now set detector
public void initDetector() {
mDetector = new ColorBlobDetector();
mSpectrum = new Mat();
mBlobColorRgba = new Scalar(255);
mBlobColorHsv = new Scalar(255);
SPECTRUM_SIZE = new org.opencv.core.Size(500, 64);
CONTOUR_COLOR = new Scalar(0, 255, 0, 255);
mDetector.setHsvColor(converScalarRgba2HSV(new Scalar(0,255,255,255)));
Imgproc.resize(mDetector.getSpectrum(), mSpectrum, SPECTRUM_SIZE, 0, 0, Imgproc.INTER_LINEAR_EXACT);
mIsColorSelected = true;
}
now pass an image to a detector object
Mat mRgba = new Mat(inputFrame.height(), inputFrame.width(), CvType.CV_8UC4);
mRgba = inputFrame;
mDetector.process(mRgba);
List<MatOfPoint> contours = mDetector.getContours();
Log.e(TAG, "Contours count: " + contours.size());
drawContours(mRgba, contours, -1, CONTOUR_COLOR);
return mRgba;
Happy Codeing !!!
I am working on opencv in android and i want to change eye pupil color through Hue channel and i achieve this already but the problem is that the region i detected is in rectangle but i want this region circular as eye pupil is circular region. kindly help how i achieve this.
private Mat get_template(CascadeClassifier clasificator, Rect area,int size){
Mat template = new Mat();
Mat mROI = mGray.submat(area);
MatOfRect eyes = new MatOfRect();
Point iris = new Point();
Rect eye_template = new Rect();
clasificator.detectMultiScale(mROI, eyes, 1.15, 2,Objdetect.CASCADE_FIND_BIGGEST_OBJECT|Objdetect.CASCADE_SCALE_IMAGE, new Size(30,30),new Size());
Rect[] eyesArray = eyes.toArray();
for (int i = 0; i < eyesArray.length; i++){
Rect e = eyesArray[i];
e.x = area.x + e.x;
e.y = area.y + e.y;
Rect eye_only_rectangle = new Rect((int)e.tl().x,(int)( e.tl().y + e.height*0.4),(int)e.width,(int)(e.height*0.6));
mROI = mGray.submat(eye_only_rectangle);
Mat vyrez = mRgba.submat(eye_only_rectangle);
Core.MinMaxLocResult mmG = Core.minMaxLoc(mROI);
Core.circle(vyrez, mmG.minLoc,2, new Scalar(255, 255, 255, 255),2);
iris.x = mmG.minLoc.x + eye_only_rectangle.x;
iris.y = mmG.minLoc.y + eye_only_rectangle.y;
eye_template = new Rect((int)iris.x-size/2,(int)iris.y-size/2 ,size,size);
Core.rectangle(mRgba,eye_template.tl(),eye_template.br(),new Scalar(255, 0, 0, 255), 2);
template = (mGray.submat(eye_template)).clone();
return template;
}
return template;
}
Some potential solutions:
the simplest, although it might not be very robust is to calculate the inscribed circle (the circle bound by the rectangle) and change it's color - if your pupil detection is very accurate this solution may work fine.
a more robust solution would be to detect the area of the pupil based on color or gradient (edge detection)